Slice assurance within a mobile network

ABSTRACT

In general, techniques are described for slice assurance within a mobile network. In some examples, a method includes obtaining, by a slice assurance function (SAF) executed by a device, key performance indicator (KPI) values for a first slice of a plurality of slices implemented by a plurality of base stations serving a tracking area of a mobile network; determining, by the SAF, based in part on the KPI values for the first slice, a service level agreement (SLA) for the first slice has not been met; re-allocating, by the SAF in response to the determining, slice resources associated with any of the plurality of slices to compute a new slice configuration parameter for the first slice; and reconfiguring, by the SAF, at least one of the plurality of base stations to implement the new slice configuration parameter for the first slice.

This application is a continuation of U.S. patent application Ser. No.17/105,833, filed 27 Nov. 2020, which claims the benefit of U.S.Provisional Patent Application No. 62/940,887, filed 27 Nov. 2019, theentire content of which is herein incorporated by reference.

TECHNICAL FIELD

The disclosure relates to mobile networks and, more specifically, toslices implemented within mobile networks.

BACKGROUND

One of the primary technical challenges facing mobile operators today isthe ability to deliver a wide array of network performancecharacteristics which future applications and services will demand. Toname a few, bandwidth, latency, packet loss, security, and reliabilitywill greatly vary from one service to the other. Emerging applicationssuch as remote operation of robots, massive IoT, and self-driving carsrequire connectivity, but with vastly different characteristics. Thecombination of architecture flexibility, software programmability, theneeds of different vertical segments (medical, factories, military,public safety, etc.) and various types of applications have led to thecreation of the concept of network slicing in 5G mobile networks. Anetwork slice provides a convenient way to completely segment thenetwork to support particular types of services or businesses.Furthermore, each slice is optimized according to capacity, coverage,connectivity, security, and performance characteristics such as delay.Since the slices can be isolated from each other, as if they arephysically separated both in the control and user planes, the userexperience of the network slice will be the same as if being on aseparate network.

A network slice within the mobile operator's domain spans the softwareapplications running on network nodes, the core network components, thetransport network, and the radio access network (RAN) components. The3GPP standards architected a sliceable 5G infrastructure to provide manylogical network segments over a common single physical network (see3GPP's TR 28.801 document titled ‘Telecommunication management; Study onmanagement and orchestration of network slicing for next generationnetwork’). The technologies such as software defined networking (SDN),wherein control plane (CP) and user plane (UP) are separated, andnetwork function virtualization (NFV) are the key enablers for slicingtraditional network structures. Customizable and virtualized networkcomponents can be stitched together, using only software, to provide theright level of connectivity.

The 5G standardization efforts have gone into defining specific slicesand their Service Level Agreements (SLAs) based on application/servicetype. For example, the user equipment (UE) can now directly specify itsdesired slice using a new field in the control messages called NetworkSlice Selection Assistance Information (NSSAI). A subfield of NSSAI isSlice/Service Types (SST) that is used to indicate the slice type. Thestandards already define most commonly usable network slice types andreserve the corresponding standardized SST values (see 3GPP TS 23.501).For example, SST values of 1, 2 and 3 correspond to slice types ofenhanced Mobile Broadband (eMBB), ultra-reliable and low-latencycommunications (uRLLC), and massive IoT (MIoT), respectively. The Accessand Mobility Management Function (AMF) of the core network retrieves theslices that are allowed by the user's subscription and interacts withthe Network Slice Selection Function (NSSF) of the core network toselect the appropriate network slice instance for that traffic.Furthermore, 3GPP specified new slice management functions such asNetwork Slice Management Function (NSMF) and Network Slice SubnetManagement Function (NSSMF), whose sole role is to create, manage andmonitor slice instances within the mobile operator's network forusers/applications. The slice catalogue and all slice-specific servicelevel agreements (SLAs) are stored within NSMF. Specific SLAs thatcorrespond to the slice segments/subnets are stored within the NSSMF.

The SLAs are classified as contractual (static) SLAs and network(dynamic) SLAs. The static SLAs simply define the legal and financialterms and conditions between the slice-user and the operator as itapplies to activation, operation, penalties, and termination of theslice. These SLAs do not change with changing network conditions andtherefore stay static. A network SLA, on the other hand, mainly definesa quality of service (QoS) requirement. These broadly include sliceavailability/reliability (service uptime as a percentage of overalltime), slice throughput (bits per second), packet latency (average andmaximum packet delay in milliseconds), packet loss (percentage of lostpackets over total packets in a defined time interval), capacity (bitsper second per km2), etc. These network slice SLAs are well documented(see 3GPP TS 22.261). There are Key Performance Indicators (KPIs) thatare collected from the network segments, in band or out of band, tomeasure the fulfilment of these SLAs over time. Network monitoring isthe task of the Service Management and Orchestration (SMO) system and iswell-defined within 3GPP standards documents.

SUMMARY

In general, techniques are described for slice assurance within a mobilenetwork. During the life-time of a network slice, from initiation totermination, its service level agreements (SLAs) must be met in a mobilenetwork, especially in the radio access network (RAN) side over aTracking Area (TA), wherein each area is formed by a plurality of cellsthat must be able to meet the SLAs under time-varying packet flowvolumes. A new control network function, called Slice Assurance Function(SAF), and its interface to the base stations (e.g., gNodeBs) aredefined, in some examples for both near real-time (near-RT) and nonreal-time (non-RT) coordination of cell slice resource distributionacross those plurality of cells with the aim of fulfilling a slice'sRAN-specific SLAs within the area.

For example, the SAF may first determine target RAN-specific slice KeyPerformance Indicators (KPIs) that must be monitored within the TA foreach slice instance and their respective thresholds, and the SAF enablesactivation of relevant Performance Management (PM) jobs within eachgNodeB it controls. The SAF monitors these KPIs and checks against thethresholds, and based on the checks the SAF may repeatedly fine-tuneslice resource distribution to gNodeBs. When the mobile traffic densityor characteristics changes within the TA over time, the SAF computes anew distribution of available resource parameters at any one or more ofRAN protocol layers 1, 2 and 3, and sends reconfiguration commands togNodeBs using the new interface. In this way, the SAF may facilitate thepractical application of an over-area and over-time slice resourceredistribution. The SAF may retrieve the slice attributes and per-sliceSLAs from the Network Slice Subnet Management Function (NSSMF), sendsslice-specific notifications to responsible parties when/if the SLAscannot be met by resource reconfiguration, and sends PM jobs for KPImonitoring to the gNodeBs.

In some examples, a method includes obtaining, by a slice assurancefunction (SAF) executed by a device, key performance indicator (KPI)values for a first slice of a plurality of slices implemented by aplurality of base stations serving a tracking area of a mobile network;determining, by the SAF, based in part on the KPI values for the firstslice, a service level agreement (SLA) for the first slice has not beenmet; re-allocating, by the SAF in response to the determining, sliceresources associated with any of the plurality of slices to compute anew slice configuration parameter for the first slice; andreconfiguring, by the SAF, at least one of the plurality of basestations to implement the new slice configuration parameter for thefirst slice.

In some examples, a slice assurance function (SAF) for a mobile networkcomprises a slice performance collector comprising processing circuitryand configured to obtain key performance indicator (KPI) values for afirst slice of a plurality of slices implemented by a plurality of basestations serving a tracking area of the mobile network; a sliceoptimizer subsystem comprising processing circuitry and configured todetermine, based in part on the KPI values for the first slice, aservice level agreement (SLA) for the first slice has not been met,wherein the slice optimizer subsystem is further configured tore-allocate, in response to the determining, slice resources associatedwith any of the plurality of slices to compute a new slice configurationparameter for the first slice; and a slice control actions subsystemcomprising processing circuitry and configured to reconfigure at leastone of the plurality of base stations to implement the new sliceconfiguration parameter for the first slice.

In some examples, a mobile network comprises a plurality of basestations comprising respective Slice Assurance Function clients and aSlice Assurance Function comprising processing circuitry and configuredto obtain key performance indicator (KPI) values for a first slice of aplurality of slices implemented by a plurality of base stations servinga tracking area of a mobile network; determine, based in part on the KPIvalues for the first slice, a service level agreement (SLA) for thefirst slice has not been met; re-allocate, in response to thedetermining, slice resources associated with any of the plurality ofslices to compute a new slice configuration parameter for the firstslice; and communicate, via an interface, the new slice configurationparameter for the first slice to one of the Slice Assurance Functionclients to cause the one of the Slice Assurance Function clients toreconfigure the corresponding base station to implement the new sliceconfiguration parameter for the first slice.

The details of one or more examples of this disclosure are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an example logical 5G RANarchitecture.

FIG. 2 is a block diagram showing a distributed gNodeB Architecture fora 5G mobile network.

FIG. 3A is a block diagram illustrating one type of slice resource usagereconfiguration across two slices, in accordance with techniques of thisdisclosure.

FIG. 3B is a block diagram illustrating another type of slice resourceusage reconfiguration across two slices, in accordance with techniquesof this disclosure.

FIG. 3C is a block diagram illustrating one type of the slice resourceusage reconfiguration across two gNodeBs for the same slice, inaccordance with techniques of this disclosure.

FIG. 3D is a block diagram illustrating another type of the sliceresource usage reconfiguration across two gNodeBs for the same slice, inaccordance with techniques of this disclosure.

FIG. 4A is a block diagram illustrating an example system with the SAF,the SAF client and the E2⁺⁺ interfaces for near-RT controls, inaccordance with techniques of this disclosure.

FIG. 4B is a block diagram illustrating an example system with the SAF,the SAF client and the O1⁺⁺ interfaces for non-RT controls, inaccordance with techniques of this disclosure.

FIG. 5 is a block diagram of SAF, in accordance with techniques of thisdisclosure.

FIG. 6 is a flowchart illustrating an example method of configuringinitiating KPI measurements, in accordance with techniques of thisdisclosure.

FIG. 7 is a flowchart illustrating an example method of reconfiguringRAN resources using SAF, in accordance with techniques of thisdisclosure.

Like reference characters denote like elements throughout thedescription and figures.

DETAILED DESCRIPTION

In general, a mobile operator's SLA may be defined on a per slice basisand comprises the path from the UE to the exit point from the mobilenetwork. That exit point may be the destination site, or it may be agateway function to the public Internet. However, a slice is comprisedof many segments even within the mobile network. That SLA often hasdifferent segments, such as

a) a RAN segment (including both fronthaul and midhaul components)

b) a transport segment (also known as backhaul)

c) a core network segment

Each of these segments contributes to an SLA in different ways. Forexample, latency is determined by adding up the delay of packets withinall said segments. The availability and packet loss are determinedsimilarly, i.e., by adding up the contribution of all segments. To thecontrary, the throughput and capacity must be identical across allsegments. From the slice SLA, NSMF must derive the RAN-specific sliceSLA portion that will be controlled by the system according totechniques described herein. The RAN-specific SLAs may be stored withinand available for access from the NSMF or NSSMF.

The 5G-radio access network has a distributed architecture. The basestation, also known as gNodeB, is divided into three functionalcomponents: Central Unit (CU), Distributed Unit (DU) and Radio Unit(RU), which can be deployed in various configurations. The CU performsthe upper layer protocol processing. The DU performs lower layerprotocol processing. Using the newly defined open F1 interface,different DU and CU vendors can now interoperate. The CU is also dividedinto the control plane and user plane functions, CU-CP and CU-UP,respectively. While CU-CP controls a plurality of CU-UPs using the E1interface; the CU-CP also controls all of its subtending DUs using theF1-C interface. The RU that manages the RF layer has antenna arrays ofvarious sizes and shapes. Depending on operator and servicerequirements, the gNodeB can be deployed monolithically, i.e., RU, DUand CU reside within the cell site, or these functionalities can bedistributed across sites, for example, RU and DU may reside in a cellsite while the CU resides in the edge cloud site controlling a pluralityof these distributed DUs. Since the DU-CU interface is more tolerant todelay, they can be separated by distance, but it is more likely that theRUs and DUs will be collocated given the interface would be more delaysensitive. The trade-off between implementations depends on the trafficrequirements and economies of these components. For simplicity, withinthis document, the term gNodeB or base station may be usedinterchangeably and without distinguishing how its distributedcomponents are deployed within cell sites.

The 5G standards define a Registration Area (RA) that comprises aplurality of Tracking Areas. The concept of Tracking Area comes from 4Gnetworks, while the Registration Area (RA) is newly defined in 5G. EachTracking Area (TA) is comprised of a plurality of cells. Each cell isidentified by the cell ID. A User Equipment (UE) is said to be within acell, a TA and a RA, at any point in time. A UE is also allowed tosupport up to 8 different network slices that must be supported by thecell/TA/RA infrastructure.

First, the UE registers with AMF, which provides the UE with the TA Id(TAI) and thus the registration area it is in. The slice is describedwith Service Level Agreements (SLAs) that are applicable throughout aTracking Area. However, the Tracking Area is comprised of a plurality ofcells, and the UEs radio access network conditions and densitydistribution across these different cells of the Area changes over time.There is no mechanism defined within the standards as to how to assurethat the RAN-specific slice SLAs are met across cells as the trafficvolumes change. Furthermore, the network KPIs are defined on anend-to-end basis including both the RAN and core network segments. Insome examples, the techniques include deriving RAN-specific slice KPIcounterparts to monitor and validate these RAN-specific SLAs.

According to some examples of this disclosure, example Slice AssuranceFunctions (SAFs) and example methods are developed so that theRAN-specific slice SLAs are met over-time and over-area (e.g., TrackingArea) when there is a large number of cells in an area. SAF collectsKPIs from all gNodeBs, either directly or via service management andorchestration (SMO) system of the 5G network, and compares withthresholds associated with each SLA component's fulfillment, and makes adetermination if all SLAs are met across all cells per TA over time. Ifnot, SAF uses an algorithm to determine the redistribution ratio ofradio access network resources across the cells by computingdistribution of the slice resources per cell across a plurality ofslices. Doing so, SAF achieves a dynamic radio resource allocation(DRRA) across cells of a Tracking Area.

According to some examples of this disclosure, algorithms fordetermining redistribution described herein may (i) re-allocate sliceresources unused by other slices to those slices that need the extraslice capacity to meet the SLA on the same gNodeB, (ii) re-allocateslice resources that are being used by slices of lower priority to thoseslices with higher priority even when both slices need the extracapacity to meet the SLA, and/or (iii) re-allocate slice resourcesacross a plurality of gNodeBs by diverting slice traffic to othergNodeBs (or components) when extra capacity is needed to meet the SLA.All of these actions may be performed in either near real-time, i.e.,under 1 second or in non real-time, i.e., over 1 second, depending onthe implementation choices. Different examples may perform anycombination of (i), (ii), and (iii).

According to some examples of this disclosure, the Slice AssuranceFunction (SAF) interfaces with all gNodeBs (or their subcomponents) tosend reconfiguration commands. These commands are received by a ‘SAFclient’, a software subsystem implemented in each gNodeB, to receivesaid reconfiguration commands and to enable activation of configurationchanges in the gNodeB, accordingly.

According to some examples of this disclosure, SAF is implemented as asubcomponent of RAN Intelligent Controller (RIC) that is well known inprior art (also known as RAN Edge-Cloud Controller). Doing so, SAFleverages RIC's already defined and implemented interfaces to SMO and toNSMF/NSSMF for collection of KPIs, Tracking Area's RAN-specific SLAs andfor alarm notification. The interface between SAF and each gNodeB isdenoted as E2++, an extended version of the E2 interface that is definedbetween RIC and gNodeB. In this example, the control of gNodeB in innear real-time.

According to some examples of this disclosure, SAF is implemented as asubcomponent of the SMO. Doing so, SAF leverages SMO's internalNSMF/NSSMF, Performance Management function for KPI collection and,Alarm/Fault Management function for alarm notification. The interfacebetween SAF and each gNodeB is denoted as O1++, an extended version ofthe O1 interface that is defined between SMO and gNodeB. In thisexample, the control of gNodeB may be in non-real-time.

According to some examples of this disclosure, a subcomponent of SAF(SAF_(SMO)) is implemented within the SMO and another subcomponent isimplemented within the RIC (SAF_(RIC)) to ensure a coordinated near-RTand non-RT controls of the gNodeBs. Also, the A1 interface between MCand SMO is extended as A1++ to support the communications between thesetwo subcomponents of SAF. In such a more complex architecture, theSAF_(SMO) engages in the algorithms/methods (such as those usingartificial intelligence/machine learning) to determine the neededcontrols, and communicate these control actions and policies toSAF_(RIC) (using A1++), which executes them in near-RT. SAF mayimplement controls both in near-RT and non-RT on a case-by-case basis.In this scenario, the interface between SAF_(SMO) and gNodeB is O1++,while the interface between SAF_(RIC) and gNodeB is E2++.

The 5G RAN supports Orthogonal Frequency-Division Multiplexing (OFDM) inthe physical layer with different numerologies (e.g. differentsubcarrier spacing and cyclic prefix lengths) and adaptable time andfrequency frame structures, meaning selectable slot durations anddynamic assignment of DL/UL transmission direction. Moreover, the UEsthat are served by the same cell can be instructed to receive ortransmit using only a subset of the cell resource grid. Aforementionedradio parameters are all adjustable in the RAN protocol layers.Furthermore, the reconfiguration of slice parameters within a cell inthe radio network is achieved simply by changing, for example, theassignment of the share of the cell resource grid over time andfrequency axis. At the upper layers of the protocol stack, the sliceSLAs are met by the proper handling of Data Radio Bearers (DRBs), forexample, by specific scheduling rules and/or radio protocol stackconfiguration for the corresponding DRBs and immediate scheduling ofURLLC type flows. These are exemplary gNodeB layer 1, layer 2 and layer3 parameters that can be adjusted within each cell to deliver the sliceSLAs, some of which are defined in terms of performance requirementssuch as throughput, energy efficiency, latency and reliability. Theserequirements are defined to assess SLAs of each slice, whichindividually need proper handling in gNodeB protocol layers. In the caseof throughput requirement, slice-based Physical Resource Block (PRB)utilization, link adaptation scheme controls, Modulation and CodingScheme (MCS) levels and Transport Block (TB) sizes are some of theexemplary configurable parameters. Similarly, the slices withhigh-energy efficiency requirement can be adjusted with DiscontinuousReception (DRX) configuration. As mentioned before, energy efficient UEscan be configured with low bandwidth to prevent energy consumingwideband operations enabled by 5G New Radio (NR). Switching betweenBandwidth Part (BWP) is simply guided by the policy received from SAF.Concerning latency requirements, non-slot based scheduling andpreemptive scheduling are supported by 5G NR. If utilization of theResource Blocks in a BWP is high, SAF manages the preemption policy,which will allow high priority delay sensitive services to be servedthrough interruption of the resource allocation to other delay-tolerantor less prioritized services e.g. mobile broadband. In order to supportreliability requirement of the slices, alternative MCS table with lowspectral efficiency has been introduced. Based on the reliabilityrequirement of slices, SAF manages dynamic MCS table signaling in thedownlink and uplink data transmission. Related to reliability, PacketData Convergence Protocol (PDCP) might be configured to generateduplicate packets, which are routed to different RLC entities. Anothermethod for increasing reliability is configuring the UEs with receivingPhysical Data Shared Channel (PDSCH) with repetition in consecutiveslots. Advantage of using repetition is that latency is decreased sincebase station is not required to wait for the HARQ acknowledgement fromthe UE while increasing the probability of successful reception. Each ofthe above parameters is a non-exhaustive list of slice parameters thatparameterize and affect slice operation. Slice parameters may also bereferred to herein as “slice configuration parameters,” for the SAF mayconfigure components of the mobile network to operate according to theslice parameters.

In some examples, SAF obtains the number and type of slices supported byeach Tracking Area and associated RAN-specific slice SLAs bycommunicating with the Network Slice Subnet Management Function (NSSMF)that is responsible for the specific Tracking Area. The SAF also sendsslice notifications, when there are violations, such as warnings andalarms to subscribed entities, which can be NSSMF and/or ServiceManagement & Orchestration (SMO) depending on operator deployment. TheSAF further communicates with SMO so that it can receive policies, whichwill be used for target RAN slice KPI derivation. Depending on theoperator's choice, NSSMF and NSMF may be implemented as a component ofthe SMO, or they may be deployed as separate functions.

The functionalities of SAF can be implemented (i) integrated into theRAN Intelligent Controller (MC) for near RT (near-RT) controls, (ii)integrated into the SMO for non RT controls (non-RT), (iii) integrated(as split functions) across SMO and RIC for both near RT and non RTcontrols, or (iv) as a separate Virtual or Physical Network Function(VNF/PNF) that communicates directly with the RIC and SMO for possiblyboth near RT or non RT controls on a case by case basis. An exampleinterface towards gNodeB for (i) above is the extended version of the E2interface, which is currently defined for collecting near real-timeinformation (e.g., UE basis and Cell basis) from gNodeBs and forproviding value added services. In an example, E2 is extended to sendreconfiguration commands, and denoted as E2++. Another exemplaryinterface towards gNodeB for (ii) above is the extended version of theO1 interface, which is currently defined for non real-time interactionswith the gNodeBs. In an example, O1 is extended to send reconfigurationcommands, and denoted as O1++. Another exemplary interface is anextension of A1 interface between RIC and SMO to send commands betweensubcomponents of SAF implemented within RIC and SMO, and denoted byA1++. Furthermore, a completely new interface may also be designedbetween the SAF and SAF client that is not reliant on the existinginterfaces such as O1 and E2.

The Key Performance Indicators (KPIs) are collected using, e.g., in-bandand/or out-of-band data collection methods from gNodeBs, at RAN protocollayers 1, 2 and 3 gathered either using the actual user packet flows orsynthetically generated packet flows. These KPIs may further be brokendown into per RU, DU and CU components, and midhaul and fronthaulfacilities, to factor in any delay, packet loss or availabilityassociated with these specific subcomponents of the RAN.

An example algorithm of SAF that may compute the RAN-specific sliceresource reallocation is the computation of redistribution weights thataccommodates traffic bursts by using the concept of a ‘range’ ofresources (e.g., 20%±5%,). These ranges simply allow for minor SLAviolation incidences that create non-critical conditions and do notrequire any RAN slice resource adjustments. Doing so, frequentreconfigurations of the gNodeBs are avoided.

An electronic device (e.g., gNodeB, UPF-User Plane Function, AMF,controller, etc.) stores and transmits (internally and/or with otherelectronic devices over a network) code (composed of softwareinstructions) and data using machine-readable media, such asnon-transitory machine-readable media (e.g., machine-readable storagemedia such as magnetic disks; optical disks; read only memory; flashmemory devices; phase change memory) and transitory machine-readabletransmission media (e.g., electrical, optical, acoustical or other formof propagated signals—such as carrier waves, infrared signals). Inaddition, such electronic devices include hardware, such as a set of oneor more processors coupled to one or more other components—e.g., one ormore non-transitory machine-readable storage media (to store code and/ordata) and network connections (to transmit code and/or data usingpropagating signals), as well as user input/output devices (e.g., akeyboard, a touchscreen, and/or a display) in some cases. The couplingof the set of processors and other components is typically through oneor more interconnects within the electronic devices (e.g., busses andpossibly bridges). Thus, a non-transitory machine-readable medium of agiven electronic device typically stores instructions for execution onone or more processors of that electronic device. One or more parts ofexample instances of the techniques of this disclosure may beimplemented using different combinations of software, firmware, and/orhardware.

As used herein, a network device such as a base station, switch,controller, or a control function is a piece of networking component,including hardware and software that communicatively interconnects withother equipment of the network (e.g., other network devices, and endsystems). Switches provide network connectivity to other networkingequipment such as switches, gateways, and routers that exhibit multiplelayer networking functions (e.g., routing, layer-3 switching, bridging,VLAN (virtual LAN) switching, layer-2 switching, Quality of Service,and/or subscriber management), and/or provide support for traffic comingfrom multiple application services (e.g., data, voice, and video).

The User Equipment (UE) is a user device such as a cellular phone, pad,a mobile sensor, a computer or another type of equipment that wirelesslyconnects to the mobile network. Any physical device in the network has atype, location, ID/name, Medium Access Control (MAC) address, andInternet Protocol (IP) address. Furthermore, a physical device can hosta collection of VNFs, each identified for example by a virtual portnumber and/or virtual IP address.

Note that while the illustrated examples in the specification discussmainly 5G networks relying on SDN, and NFV, examples that implement thetechniques described herein may also be applicable in other kinds ofnetwork (mobile and non-mobile) that are sliceable.

FIG. 1 illustrates an exemplary radio access network (RAN) 10 having aRegistration Area (RA) 14 with three Tracking Areas 11, 12, and 13.Tracking Area 11 has cells 101, 102, 103 and 104. Tracking Area 12 hascells 1111, 1112, 1113 and 1114. Finally, Tracking Area 13 has cells2111, 2112, 2113, and 2114. A unique Tracking Area Id (TAI) identifieseach Tracking Area. The RA 14 is defined as a set of Tracking Areas. Aunique Cell Id identifies each cell within a TA.

Each cell within a Tracking Area has an associated base station(gNodeB). The 5G base station architecture distributes variouscomponents that form a base station. The Central Unit (CU), DistributedUnit (DU) and Radio Unit (RU) are the basic components, as illustratedin FIG. 2. The RUs support the radio interface, and for that, carry theantenna arrays. The brain of the gNodeB is the CU, which mainly performslayer 3 operations, which is controlled by CU-CP. The CU-CP controlsboth the CU-UP and all its subtending DUs. Shown in the figure is asingle CU with three subtending DUs: DU #2 has three RUs (RU #1, #2 and#3), wherein DU #1 and DU #2 have integrated RUs. The gNodeB can bedeployed in various configurations depending on cell requirements andtraffic types.

FIGS. 3A, 3B, 3C and 3D illustrate simplified diagrams to contrast theslice resource utilization according to the existing 3GPPspecifications, and slice resource reconfiguration according totechniques of this disclosure within a simple TA that has two cell sites(supported by gNodeB 1 and gNodeB 2, respectively), and two sliceinstances: slice-1 and slice-2. The total throughput initiallyconfigured for both slice-1 and slice-2 is 3 Mbps per slice, which isconfigured on both gNodeB 1 and gNodeB 2 that make up the Tracking Areain this simple example.

FIG. 3A shows the slice throughput that materializes over time forslice-1 and slice-2 on gNodeB 1. We note that while the traffic ofslice-2 is relatively light, the slice-1 traffic exceeds the configuredthroughput between times t₁ and t₂. By performing the over-time resourceconfiguration according to this example, throughput is ‘borrowed’ fromslice-2 and shifted to slice-1 on gNodeB 1 by reconfiguring thethroughput parameters of gNodeB 1 only.

FIG. 3B shows another type of slice throughput that materializes overtime0 for slice-1 and slice-2 on gNodeB 1. While the traffic of bothslices 1 and 2 are high, slice-1 is a higher priority slice. The trafficfor both slices exceed the configured throughput between times t₁ andt₂. By performing the over-time resource configuration according to thisexample, the throughput is borrowed from slice-2 that is of lowerpriority and shifted to slice-1 on gNodeB 1 by reconfiguring thethroughput parameters of gNodeB 1 only.

FIG. 3C shows the slice throughput that materializes over time forslice-1 on gNodeB 1 and gNodeB 2. We note that while the traffic ofslice-1 is relatively light on gNodeB 2, it exceeds the configuredthroughput in gNodeB 1. By performing the over-Area resourceconfiguration according to techniques of this disclosure, the throughputis borrowed from gNodeB 2 by forcing users of slice-1 on gNodeB 1 tohandover to gNodeB 2. For example, SAF 400 may reconfigure gNodeB 1and/or gNode B with different antenna power or change load balancingparameters of the gNodeBs to force handover.

FIG. 3D shows another instance of slice throughput that materializesover time for slice-1 on gNodeB 1 and gNodeB 2. The traffic of slice-1is high and exceeds the configured throughput on both gNodeB 1 andgNodeB 2. By performing the over-area resource configuration accordingto this example, the throughput is borrowed from gNodeB 2 to meet theexcess slice-1 traffic on gNodeB 1. Although slice-1's throughput SLA isstill not met on gNodeB 2, it is met on gNodeB 1 by doing so. Thesereconfigurations illustrate how dynamic weights are assigned tothroughput distribution across base stations over an area and acrossslices over-time according to this example. A higher weight for theslice throughput is given to slice-1 between times t₁ and t₂ (25% more)on gNodeB 1 in FIGS. 3A and 3B. Similarly, a higher weight to gNodeB 1throughput is given compared to gNodeB 2's for slice-1 (25% more) inFIG. 3D. Doing so, both over-time and per-area slice throughput isimproved.

Let I={1, 2 . . . n} denote the set of slices in the TA and C={1, 2 . .. m} denote the set of cells in the TA. For each slice i∈I, let d_(i)indicate the guaranteed data rate requirement for each UE indicated inthe SLA and let the set U_(i) be the UEs receiving service from slice i.In this scenario, R_(u) indicates the experienced data rate of the UE uE

Hence, the following expression is an example performance indicatorwhich shows whether the UEs in slice i∈I is reaching the slice-specificguaranteed data rate requirement.

$\frac{1}{❘U_{i}❘}{\sum\limits_{u \in U_{i}}{x_{u}*100{\forall{i \in {I.}}}}}$

Wherein x_(u) is the normalized rate of UE u∈U_(i), which is boundedbetween 0 and 1.

$x_{u} = \left\{ {\begin{matrix}{1,} & {{{if}\ \frac{R_{u}}{d_{i}}} \geq 1} \\{\frac{R_{u}}{d_{i}},} & {otherwise}\end{matrix}.} \right.$

The result of this expression is used by SAF to update RAN specificparameters, which effects the UE rate directly. As mentioned before, PRBallocation for each slice is one of the parameters used for throughputrequirements. Let w_(i) ^(j) indicates the percentage of the PRBsallocated to slice i∈I and cell i∈C. Accordingly,

${\sum\limits_{i \in I}w_{i}^{j}} = {100\%{\forall{j \in {C.}}}}$

Different slices within a TA, and the PRB distribution across theplurality of cells forming the TA can be represented in a matrix for abetter understanding of the results of over-area and over-timereconfiguration. The columns represent different types of slices in theTA while rows represent the cells/gNodeBs within the TA that areconfigurable. In an exemplary scenario, we assume that there are fourslices (I₁, I₂, I₃, and I₄) and three cells (C₁, C₂, and C₃) in the TA.Slice 3 is a ‘high-priority’ slice (from one or more SLAs perspective).The resource distribution at time t=t₀ in percentages is shown below.Note that the total resources per cell, across all slices, must add upto 1:

I₁ I₂ I₃ I₄ C₁ 30% 20% 30% 20% C₂ 0 0 60% 40% C₃ 10% 20% 50% 20%

At time t=t₁, the traffic of slice 3 has significantly increased incells C₁. SAF checks if guaranteed throughput per UE in slice 3 is met.Additionally, SAF checks radio resource utilization and load of theslice e.g. average number of PRBs used in the previous time interval fordata traffic. According to an example of the techniques of thisdisclosure, the C₁ resources are borrowed from I₁ and I₂ and shiftedonto I₃ providing a higher percentage usage to slice 3.

I₁ I₂ I₃ I₄ C₁ 10% 10% 60% 20% C₂ 0 0 60% 40% C₃ 10% 20% 50% 20%

The optimization algorithm of SAF computes the resource distributionconfiguration parameters for each cell and each slice during eachobservation period. Note that the SAF optimization algorithm may use asimple heuristic algorithm, many of which are known in prior art, or asophisticated machine learning technique using Artificial Intelligence(AI) such as Deep Learning that learns and improves the slice traffic'sresponse to various resource distribution actions over time.

FIG. 4A illustrates Tracking Area 420 with many cell sites: gNodeB 422a, 422 b, 422 c . . . 422 n. The distributed components of gNodeB 422 aare also shown. These are CU-CP 452 a, CU-UP 432 a and DUs 442 a-k.CU-CP 452 a controls CU-UP 432 a using E1 interface, and controls DU 442a using F1-C interface. Example components for implementing techniquesof this disclosure may include: (i) SAF 400, which makes decisions onresource redistribution over-time and area based on the RAN-specificSLAs and collected KPIs, and controls all said base stations, (ii)interface between SAF 400 and all gNodeBs, the E2⁺⁺ interface, and (iii)SAF client 451 a,b . . . n that receive the reconfiguration commandsthrough E2⁺⁺ interface and in some cases enable the execution innear-RT.

SAF 400 obtains each slice type supported within the Tracking Area aswell as the SLAs associated with each slice from the Network SliceSubnet Management Function (NSSMF) 404 through RIC 410. NSSMF 404 isassumed to obtain the RAN-specific slice SLA catalogue and specific SLAcomponent information from NSMF 406.

SAF 400 collects near real-time performance data related to the sliceSLAs either directly from all base stations using the E2⁺⁺ interface, orvia the SMO 408 using A1 interface 458. SMO 408 receives notificationssuch as warnings and alarms from SAF 400 when the slice SLA cannot bemet using the same interface.

Another example of SAF 400 is illustrated in FIG. 4B, in which SAF is acomponent of the SMO (instead of RIC). In this scenario, the interfaceused for reconfiguration towards the gNodeBs is an extended O1interface, denoted as O1⁺⁺. Because O1 is designed for non-RToperations, the consequence of this example may be less frequentreconfigurations of the gNodeBs. Although not illustrated in a separatefigure, one can easily superimpose FIGS. 4A and 4B to come up with yetanother alternate configuration wherein some components of SAF are inRIC (as in FIG. 4A), and other components are in SMO (as in FIG. 4B),which may require extension in the A1 interface between RIC and SMO tosupport intra-SAF messaging. This interface, which is called A1⁺⁺, hasdifferent capabilities depending on the functional split of SAF acrossthese two systems. Such variations in implementation are covered by thisdisclosure.

In some cases, SAF 400 may apply an artificial intelligence or othermachine learning model trained using historical KPIs to predict likelyfailures of a slice to meet the corresponding SLA. In response toidentifying an upcoming likely failure based on recent KPIs, SAF 400 mayre-distribute slice resources ahead of the predicted failure time andreconfigure the eNodeBs to avoid the failure.

FIG. 5 is a high-level block diagram of SAF 400, according to someexamples of an SAF. Slice Catalogue and Slice SLAs Database 505 ispopulated by information gathered from NSSMF. Slice PerformanceCollector 503 collecting measurements from base stations populatesReal-Time (RT) RAN Slice KPIs Database 506. Slice Parameters NRTDatabase 507 stores all the determined slice parameters as assigned toeach base station in the TA and over time. Over-Time and Over-Area SliceOptimizer Subsystem 500 calculates weights to apportion the TA's sliceSLA configuration to each base station configuration. Slice ControlActions Subsystem 501 maps the weights into proper base station controlactions and sends these actions onto base stations 422 a . . . n. Theinterface between SAF and SAF client supports simple example messagessuch as:

<ReconfigRequest {ID} {component ID} {time stamp}{param: value} {param:value} . . . {param: value}>: A request from SAF to reconfigureresources using new parametric values.

-   -   <FallBackToDefault {component ID} {time stamp}>: A request to        fall back to its original/default slice configuration.    -   <ReconfigResponseSuccess {param: value1} {param: value1} . . .        {param: value1} {component ID} {time stamp}>: A response from        gNodeB to SAF on successful implementation of reconfiguration    -   <ReconfigResponseFailure {param: value1} {param: value1} . . .        {param: value1} {component ID} {time stamp}>: A response from        gNodeB on failure of implementing the requested reconfiguration

If a slice SLA cannot be met, Slice Notification Subsystem 502 preparesan alarm or warning to send to SMO 408's Alarm Management sub-function.Subsystem 516 determines per-TA RAN-specific slice KPIs and their targetand threshold values once the slice catalogue and slice SLAs areobtained. Subsystem 515 generates Performance Management jobs (tasks) tobe executed by each gNodeB so that the slice KPIs are collected andreported for the consumption of SAF 400.

Example methods or steps for implementing techniques of the disclosureare shown in FIGS. 6 and 7. At step 601, in FIG. 6, SAF 400 collects allslice types and associated SLAs supported by the TA from NSSMF 603. SAF400 derives the slice KPIs, their target and threshold values in step611. Thereafter, SAF 400 generates the PM jobs that each gNodeB mustexecute for slice-specific KPI monitoring in step 671. Finally, in step681, RIC 410 communicates the required KPIs to each gNodeB in theTracking Area and starts collecting from each gNodeB the KPIs for SAF400's consumption.

At time T, in FIG. 7, SAF 400 collects the KPIs associated with eachslice from all base stations in step 602. In checkbox 604, SAF 400 firstchecks to determine if the slice SLA of the TA is met for each flowwithin a slice. This check and the following steps are performed for allslices, which may be done iteratively or in parallel. If the answer tocheckbox 604 is yes, then SAF 400 waits until the next time interval(T+1) in step 611 to repeat the process.

In some examples, SAF 400 optionally (as indicated by the dashed linesof 609) determines if prediction-based SLA adjustment should beperformed (step 609). If not, SAF 400 waits until the next time interval(T+1) in step 611 to repeat the process for the slice. Ifprediction-based SLA adjustment is to be performed (YES branch of step609), then SAF 400 performs step 618, which in these examples includere-optimizing slice parameters by applying predictive techniques forpredicting likely failures of a slice to meet the corresponding SLA,e.g., those described above. For instance, SAF 400 may predict that oneof the SLA for a corresponding slice will not be met and, in response,preemptively reallocate slices resources to compute new sliceconfiguration parameters for the slice. SAF 400 may then reconfigure thebase stations to implement the new slice configuration parameters forthe slice.

However, if some slice SLAs are not met (NO branch of 604), SAF 400checks to determine if the TA's total slice SLA is met over the timeperiod of T in checkbox 605. If not, an alarm is generated in step 621towards the SMO. Otherwise, in step 618, SAF 400 redistributes sliceresources across one or more base stations. This step may be executed byOver-Time and Over-Area Slice Optimizer Subsystem 500, which computesnew slice resource parameters that, when configured into the gNodeBsusing slice configuration parameters computed by SAF 400, will realizethe slice resources parameters according to the redistribution. In step622, SAF 400 checks to determine if a feasible solution toredistribution exists such that all slices can meet their associatedSLAs. If not, SAF 400 sends an alarm to the Alarm Management in the SMOin step 621. Otherwise, SAF 400 sends the new slice configurationparameters to the at least one of the base stations, in step 642, torealize the new slice resources computed for the slices. Any of theCU-CP, CU-UP, or DU(s) for the one or more base stations may receive thenew slice configuration parameters for reconfiguration of the one ormore base stations.

Abbreviations 5G: 5^(th) Generation Mobile Wireless Communication SystemAMF: Access and Mobility Function CP: Control Plane CU: Central Unit DU:Distributed Unit

gNodeB: Base Station

IoT: Internet of Things IP: Internet Protocol KPI: Key PerformanceIndicator NRF: Network Repository Function NSSAI: Network SliceSelection Assistance Information NSMF: Network Slice Management FunctionNSSMF: Network Slice Subnet Management Function PM: PerformanceManagement RA: Registration Area RU: Radio Unit QoS: Quality of ServiceRAN: Radio Access Network RIC: RAN Intelligent Controller SDN: SoftwareDefined Network SLA: Service Level Agreement SMO: Service Management andOrchestration SST: Slice Service Type TA: Tracking Area UE: UserEquipment UP: User Plane VNF: Virtualized Network Function

Computer-executable instructions include, for example, instructions anddata which cause a general-purpose computer, special-purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,components, data structures, objects, and the functions inherent in thedesign of special-purpose processors, etc. that perform particular tasksor implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random-access memory or both. The essential elements of a computer area processor for performing or executing instructions and one or morememory devices for storing instructions and data. Generally, a computerwill also include, or be operatively coupled to receive data from ortransfer data to, or both, one or more mass storage devices for storingdata, e.g., magnetic, magneto-optical disks, or optical disks.

In this specification, the term “software” is meant to include firmwareresiding in read-only memory or applications stored in magnetic storageor flash storage, for example, a solid-state drive, which can be readinto memory for processing by a processor. Also, in someimplementations, multiple software technologies can be implemented assub-parts of a larger program while remaining distinct softwaretechnologies. In some implementations, multiple software technologiescan also be implemented as separate programs. Finally, any combinationof separate programs that together implement a software technologydescribed here is within the scope of the subject technology. In someimplementations, the software programs, when installed to operate on oneor more electronic systems, define one or more specific machineimplementations that execute and perform the operations of the softwareprograms.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

These functions described above can be implemented in digital electroniccircuitry, in computer software, firmware or hardware. The techniquescan be implemented using one or more computer program products.Programmable processors and computers can be included in or packaged asmobile devices. The processes and logic flows can be performed by one ormore programmable processors and by one or more programmable logiccircuitry or processing circuitry. General and special purpose computingdevices and storage devices can be interconnected through communicationnetworks.

Some implementations include electronic components, for examplemicroprocessors, storage and memory that store computer programinstructions in a machine-readable or computer-readable medium(alternatively referred to as computer-readable storage media,machine-readable media, or machine-readable storage media). Someexamples of such computer-readable media include RAM, ROM, read-onlycompact discs (CD-ROM), recordable compact discs (CD-R), rewritablecompact discs (CD-RW), read-only digital versatile discs (e.g., DVD-ROM,dual-layer DVD-ROM), a variety of recordable/rewritable DVDs (e.g.,DVD-RAM, DVD-RW, DVD+RW, etc.), Flash memory (e.g., SD cards, mini-SDcards, micro-SD cards, etc.), magnetic or solid state hard drives,read-only and recordable Blu-Ray® discs, ultra density optical discs,any other optical or magnetic media, and floppy disks. Thecomputer-readable media can store a computer program that is executableby at least one processing unit and includes sets of instructions forperforming various operations. Examples of computer programs or computercode include machine code, for example is produced by a compiler, andfiles including higher-level code that are executed by a computer, anelectronic component, or a microprocessor using an interpreter.

While the above discussion primarily refers to microprocessor ormulti-core processors that execute software, some implementations areperformed by one or more integrated circuits, for example applicationspecific integrated circuits (ASICs) or field programmable gate arrays(FPGAs). In some implementations, such integrated circuits executeinstructions that are stored on the circuit itself

As used in this specification and any claims of this application, theterms “computer readable medium” and “computer readable media” areentirely restricted to tangible, physical objects that store informationin a form that is readable by a computer. These terms exclude anywireless signals, wired download signals, and any other ephemeralsignals.

What is claimed is:
 1. A method comprising: obtaining, by a sliceassurance function (SAF) executed by a device, key performance indicator(KPI) values for a first slice of a plurality of slices implemented by aplurality of base stations serving a tracking area of a mobile network;determining, by the SAF, based in part on the KPI values for the firstslice, a service level agreement (SLA) for the first slice has not beenmet; re-allocating, by the SAF in response to the determining, sliceresources associated with any of the plurality of slices to compute anew slice configuration parameter for the first slice; andreconfiguring, by the SAF, at least one of the plurality of basestations to implement the new slice configuration parameter for thefirst slice.
 2. The method of claim 1, wherein determining the SLA forthe first slice has not been met comprises determining the SLA for thefirst slice has not been met for at least one flow associated with thefirst slice.
 3. The method of claim 1, further comprising: outputting,by the SAF, in response to determining a total slice SLA is not met overa time period, an alarm indicating the tracking area has insufficientresources to meet the SLA for the first slice.
 4. The method of claim 1,wherein re-allocating slice resources associated with any of theplurality of slices comprises: re-allocating slice resources, unused bya second slice of the plurality of slices, from the second slice to thefirst slice to compute the new slice configuration parameter for thefirst slice.
 5. The method of claim 1, wherein re-allocating sliceresources associated with any of the plurality of slices comprises:re-allocating slice resources, in use by a second slice of the pluralityof slices that has a lower priority than the first slice, from thesecond slice to the first slice to compute the new slice configurationparameter for the first slice.
 6. The method of claim 1, whereinre-allocating slice resources associated with any of the plurality ofslices comprises: forcing handover of a user equipment (UE) that usesthe first slice from a first base station of the plurality of basestations to a second base station of the plurality of base stations thatsupports the first slice.
 7. The method of claim 6, wherein the newslice configuration parameter for the first slice comprises anidentifier for the second base station that is to serve the UE.
 8. Themethod of claim 1, wherein the at least one of the plurality of basestations comprises a SAF client, and wherein reconfiguring the at leastone of the plurality of base stations to implement the new sliceconfiguration parameter for the first slice comprises outputting, by theSAF to the SAF client, configuration commands to cause the SAF client toreconfiguring the at least one of the plurality of base stations.
 9. Themethod of claim 8, wherein the SAF and the SAF client communicate via aninterface comprising one of an O1 interface, an E2 interface, or a3GPP-based performance management interface, wherein the interface isextended to enable collection and reporting of KPI values from the SAFclient to the SAF.
 10. The method of claim 1, wherein the devicecomprises a Radio Access Network (RAN) Intelligent Controller (RIC). 11.The method of claim 10, wherein an E2 interface between the RIC and theat least one of the plurality of base stations is extended to supportcommunications between the SAF and an SAF client for the at least one ofthe plurality of base stations.
 12. The method of claim 1, wherein thedevice comprises a Service Management and Orchestration (SMO) system.13. The method of claim 12, wherein an O1 interface or a 3GPP-basedmanagement interface between the SMO system and the at least one of theplurality of base stations is extended to support communications betweenthe SAF and an SAF client for the at least one of the plurality of basestations.
 14. The method of claim 1, wherein the SAF includes SAFcomponents executed by a Radio Access Network (RAN) IntelligentController (MC) and a Service Management and Orchestration (SMO) system.15. The method of claim 14, wherein an A1 interface is extended tosupport communications between the SAF component executed by the RIC andthe SAF component executed by the SMO system.
 16. The method of claim 1,further comprising: collecting, by the SAF, SLAs for all of theplurality of slices; determining, by the SAF based on the SLAs, sliceKPIs to be monitored within the tracking area for each of the pluralityof slices; and generating and outputting, by the SAF to the plurality ofbase stations, performance management jobs to cause the plurality ofbase stations to monitor and report the slice KPIs.
 17. The method ofclaim 1, further comprising, for each additional slice of the pluralityof slices, iteratively: obtaining, by the SAF, KPI values for theadditional slice; determining, by the SAF, based in part on the KPIvalues for the additional slice, an SLA for the additional slice has notbeen met; re-allocating, by the SAF in response to the determining,slice resources associated with any of the plurality of slices tocompute a new slice configuration parameter for the additional slice;and reconfiguring, by the SAF, at least one of the plurality of basestations to implement the new slice configuration parameter for theadditional slice.
 18. The method of claim 1 further comprising:obtaining, by the SAF, KPI values for a second slice of the plurality ofslices; predicting, by the SAF, based in part on the KPI values for thesecond slice, an SLA for the second slice will not be met;re-allocating, by the SAF in response to the determining, sliceresources associated with any of the plurality of slices to compute anew slice configuration parameter for the second slice; andreconfiguring, by the SAF, at least one of the plurality of basestations to implement the new slice configuration parameter for thesecond slice.
 19. A slice assurance function (SAF) for a mobile network,the SAF comprising: a slice performance collector comprising processingcircuitry and configured to obtain key performance indicator (KPI)values for a first slice of a plurality of slices implemented by aplurality of base stations serving a tracking area of the mobilenetwork; a slice optimizer subsystem comprising processing circuitry andconfigured to determine, based in part on the KPI values for the firstslice, a service level agreement (SLA) for the first slice has not beenmet, wherein the slice optimizer subsystem is further configured tore-allocate, in response to the determining, slice resources associatedwith any of the plurality of slices to compute a new slice configurationparameter for the first slice; and a slice control actions subsystemcomprising processing circuitry and configured to reconfigure at leastone of the plurality of base stations to implement the new sliceconfiguration parameter for the first slice.
 20. A mobile networkcomprising: a plurality of base stations comprising respective SliceAssurance Function clients; a Slice Assurance Function comprisingprocessing circuitry and configured to: obtain key performance indicator(KPI) values for a first slice of a plurality of slices implemented by aplurality of base stations serving a tracking area of a mobile network;determine, based in part on the KPI values for the first slice, aservice level agreement (SLA) for the first slice has not been met;re-allocate, in response to the determining, slice resources associatedwith any of the plurality of slices to compute a new slice configurationparameter for the first slice; and communicate, via an interface, thenew slice configuration parameter for the first slice to one of theSlice Assurance Function clients to cause the one of the Slice AssuranceFunction clients to reconfigure the corresponding base station toimplement the new slice configuration parameter for the first slice.